Users increasingly desire to integrate their virtual and real-world experiences. Such integration often demands responsive user interfaces as well as high-fidelity virtual representations of real-world objects. Various machine vision technologies may be used to address both of these demands. For example, gesture-based interfaces may be implemented using depth and/or visual-imaging sensor systems (e.g., the Microsoft Kinect™, Intel RealSense™, Apple PrimeSense™, Structure Sensor™, Velodyne HDL-32E LiDAR™, Orbbec Astra™, etc.). Similarly, a variety of tools and techniques may be used to generate virtual representations of real-world objects (using various scanning devices, e.g., the Artec Eva™, Artec Shapify Booth™, Twindom Twinstant™ full body 3D scanner, etc.).
Unfortunately, the mass production of high-fidelity virtual representations of real-world objects can be difficult and expensive. Particularly, scans of real-world objects very often include undesirable artifacts originating either from neighboring objects or scanning errors. While such artifacts may be manually corrected, such correction quickly becomes unfeasible when scanning large numbers of real-world objects. Automated systems may also fail to adequately isolate the real-world object of interest from surrounding objects during the scanning process. In addition, automated systems may fail to anticipate subsequent manual corrections by artists. When virtual models include undesirable artifacts, or omit features of the real-world object, the subsequent virtual rendering may be unconvincing or otherwise suboptimal.
Consequently, when scanning a large number of real-world objects, there is a need to reduce the dependency upon manual correction so as to reduce the cost, time, and expense of the scanning process. Reliance upon manual correction mitigates the effective scanning of real-world objects in large numbers, as well as limits the number of viable high-volume scanning applications. The average user, for example, will often neither have the skills, nor the inclination, to hire an artist to make corrections. Neither will users typically wish to perform the tedious correction process themselves. However, a successful automated system may also need to anticipate subsequent manual intervention.
These limitations mitigate the seamless integration of the real and virtual worlds. Consequently, there exists a need for systems and methods to more adequately automate the creation and use of high-fidelity real-world objects in virtual environments.
The specific examples depicted in the drawings have been selected to facilitate understanding. Consequently, the disclosed embodiments should not be restricted to the specific details in the drawings or the corresponding disclosure. For example, the drawings may not be drawn to scale, the dimensions of some elements in the figures may have been adjusted to facilitate understanding, and the operations of the embodiments associated with the flow diagrams may encompass additional, alternative, or fewer operations than those depicted here. Thus, some components and/or operations may be separated into different blocks or combined into a single block in a manner other than as depicted. The embodiments are intended to cover all modifications, equivalents, and alternatives falling within the scope of the disclosed examples, rather than limit the embodiments to the particular examples described or depicted.